fit_model: Fit a Model to a List of Datasets

In power4mome: Power Analysis for Moderation and Mediation

Fit a Model to a List of Datasets

Description

Get the output of sim_data() and fit a model to each of the stored datasets.

Usage

fit_model(
  data_all = NULL,
  model = NULL,
  fit_function = "lavaan",
  arg_data_name = "data",
  arg_model_name = "model",
  arg_group_name = "group",
  ...,
  fit_out = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1)
)

Arguments

data_all	The output of sim_data(), or a sim_data class object.
model	The model to be fitted. If NULL, the default, the model stored in data_all, which should be the data generation model, will be used.
fit_function	The function to be used to fit the model. Can also be a string: "lavaan" (the default) for lavaan::sem(), and "lm" or many_lm for lmhelprs::many_lm(). Other functions can also be used if necessary.
arg_data_name	The name of the argument of fit_function expecting the dataset. Default is "data".
arg_model_name	The name of the argument of fit_function expecting the model definition. Default is "model".
arg_group_name	The name of the argument of fit_function expecting the name of the group variable. Used only for multigroup models. Default is "group".
...	Optional arguments to be passed to fit_function when fitting the model.
fit_out	If set to a fit_out object (the output of fit_model()), then all missing arguments will be retrieved from fit_out. That is, users can use fit_model(data_all = new_data, fit_out = old_out) to re-fit a model originally fitted in old_out on a new list of dataset (new_data). No need to include all other arguments.
parallel	If TRUE, parallel processing will be used to fit the models. Default is FALSE.
progress	If TRUE, the progress of model fitting will be displayed. Default is 'FALSE.
ncores	The number of CPU cores to use if parallel processing is used.

Details

By default, the function fit_model()

• extracts the model stored in the output of sim_data(),

• fits the model to each dataset simulated using fit_function, default to "lavaan" and lavaan::sem() will be called,

• and returns the results.

If the datasets were generated from a multigroup model when calling sim_data(), a multigroup model is fitted.

Value

An object of the class fit_out, which is a list of the output of fit_function (lavaan::sem() by default). If an error occurred when fitting the model to a dataset, then this element will be the error message from the fit function.

The role of fit_model()

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the model fitting step in power analysis.

See Also

See power4test() for the all-in-one function that uses this function, and sim_data() for the function generating datasets for this function.

Examples

# Specify the population model

mod <-
"m ~ x
 y ~ m + x"

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)
fit_all[[1]]

# Fit the population model using the MLR estimator

fit_all_mlr <- fit_model(data_all,
                         estimator = "MLR")
fit_all_mlr[[1]]

# Fit a model different from the population model,
# with the MLR estimator

mod2 <-
"m ~ x
 y ~ m"

fit_all_mlr2 <- fit_model(data_all,
                          mod2,
                          estimator = "MLR")
fit_all_mlr2[[1]]

power4mome documentation built on Sept. 9, 2025, 5:35 p.m.